pandas.qcut will give you the quantiles, but a histogram-like operation will require some numpy trickery which comes in handy here:
_, breaks = np.histogram(df.MAT, bins=5)
ax = df.boxplot(column='N0_YLDF', by='Class')
ax.xaxis.set_ticklabels(['%s'%val for i, val in enumerate(breaks) if i in df.Class])
The dataframe now looks like this:
N0_YLDF MAT Class
0 1.29 13.67 1
1 2.32 10.67 0
2 6.24 11.29 1
3 5.34 21.29 1
4 6.35 41.67 2
5 5.35 91.67 5
6 9.32 21.52 1
7 6.32 31.52 2
8 3.33 13.52 1
9 4.56 44.52 3
[10 rows x 3 columns]
It can also be used to get the quartile plot:
breaks = np.asarray(np.percentile(df.MAT, [25,50,75,100]))
df['Class'] = (df.MAT.values > breaks[..., np.newaxis]).sum(0)
ax = df.boxplot(column='N0_YLDF', by='Class')
ax.xaxis.set_ticklabels(['%s'%val for val in breaks])
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